# Computations
import numpy as np
import pandas as pd
import pickle

# Tools
import os
import datetime
import itertools
import pickle

# Sklearn
from sklearn.impute import SimpleImputer

# Visualisation libraries

## progressbar
import progressbar

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

import warnings
warnings.filterwarnings("ignore")

Home / Real Estate

Calgary Property Assessments

• Preprocessing
• Modeling: Stacked Modeling

House Prices: Advanced Regression Techniques

In this article, we use a dataset from Kaggle.com.

Competition Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Data Description

File descriptions

• train.csv - the training set
• test.csv - the test set
• data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here
• sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms

Data fields

Here's a brief version of what you'll find in the data description file.

Feature	Description	Feature	Description
SalePrice	the property's sale price in dollars.	HeatingQC	Heating quality and condition
MSSubClass	The building class	CentralAir	Central air conditioning
MSZoning	The general zoning classification	Electrical	Electrical system
LotFrontage	Linear feet of street connected to property	1stFlrSF	First Floor square feet
LotArea	Lot size in square feet	2ndFlrSF	Second floor square feet
Street	Type of road access	LowQualFinSF	Low quality finished square feet (all floors)
Alley	Type of alley access	GrLivArea	Above grade (ground) living area square feet
LotShape	General shape of property	BsmtFullBath	Basement full bathrooms
LandContour	Flatness of the property	BsmtHalfBath	Basement half bathrooms
Utilities	Type of utilities available	FullBath	Full bathrooms above grade
LotConfig	Lot configuration	HalfBath	Half baths above grade
LandSlope	Slope of property	Bedroom	Number of bedrooms above basement level
Neighborhood	Physical locations within Ames city limits	Kitchen	Number of kitchens
Condition1	Proximity to main road or railroad	KitchenQual	Kitchen quality
Condition2	Proximity to main road or railroad (if a second is present)	TotRmsAbvGrd	Total rooms above grade (does not include bathrooms)
BldgType	Type of dwelling	Functional	Home functionality rating
HouseStyle	Style of dwelling	Fireplaces	Number of fireplaces
OverallQual	Overall material and finish quality	FireplaceQu	Fireplace quality
OverallCond	Overall condition rating	GarageType	Garage location
YearBuilt	Original construction date	GarageYrBlt	Year garage was built
YearRemodAdd	Remodel date	GarageFinish	Interior finish of the garage
RoofStyle	Type of roof	GarageCars	Size of garage in car capacity
RoofMatl	Roof material	GarageArea	Size of garage in square feet
Exterior1st	Exterior covering on house	GarageQual	Garage quality
Exterior2nd	Exterior covering on house (if more than one material)	GarageCond	Garage condition
MasVnrType	Masonry veneer type	PavedDrive	Paved driveway
MasVnrArea	Masonry veneer area in square feet	WoodDeckSF	Wood deck area in square feet
ExterQual	Exterior material quality	OpenPorchSF	Open porch area in square feet
ExterCond	Present condition of the material on the exterior	EnclosedPorch	Enclosed porch area in square feet
Foundation	Type of foundation	3SsnPorch	Three season porch area in square feet
BsmtQual	Height of the basement	ScreenPorch	Screen porch area in square feet
BsmtCond	General condition of the basement	PoolArea	Pool area in square feet
BsmtExposure	Walkout or garden level basement walls	PoolQC	Pool quality
BsmtFinType1	Quality of basement finished area	Fence	Fence quality
BsmtFinSF1	Type 1 finished square feet	MiscFeature	Miscellaneous feature not covered in other categories
BsmtFinType2	Quality of second finished area (if present)	MiscVal	Value of miscellaneous feature
BsmtFinSF2	Type 2 finished square feet	MoSold	Month Sold
BsmtUnfSF	Unfinished square feet of basement area	YrSold	Year Sold
TotalBsmtSF	Total square feet of basement area	SaleType	Type of sale
Heating	Type of heating	SaleCondition	Condition of sale

def Header(Text, L = 100, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
         'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + Text + Style.RESET_ALL + ' ' + FORE[C] +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
    
def Line(L=100, C = 'Blue'):
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(FORE[C] + Style.NORMAL + L*'=' + Style.RESET_ALL)

def Search_List(Key, List): return [s for s in List if Key in s]

PATH = 'house-prices-advanced-regression-techniques'
Files = ['train.csv','test.csv']
#
Files_Info = pd.DataFrame()
for i in range(len(Files)):
    # Read files
    Header(Files[i])
    filename = Files[i].split('.')[0]
    globals() [filename] = pd.read_csv(os.path.join(PATH, Files[i]))
    Temp = globals() [filename].shape
    Files_Info = Files_Info.append(pd.DataFrame({'File':[Files[i]], 'Number of Instances': [Temp[0]],
                                                 'Number of Attributes': [Temp[1]]}), ignore_index = True)
    # Datetime
    Cols = globals() [filename].columns
    DateTime = Search_List('date', Cols)
    if len(DateTime)>0:
        try: DateTime.remove('date_block_num')
        except: pass
        for c in DateTime:
            globals() [filename][c] = pd.to_datetime(globals() [filename][c]) 
            del c
    # Display
    display(globals() [filename].head(5))
Line()
display(Files_Info.style.hide_index())
Line()
del Files, i, Temp, filename, Cols, DateTime 

train.csv ==========================================================================================

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

5 rows × 81 columns

test.csv ===========================================================================================

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	...	120	0	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	...	144	0	NaN	NaN	NaN	0	1	2010	WD	Normal

5 rows × 80 columns

====================================================================================================

File	Number of Instances	Number of Attributes
train.csv	1460	81
test.csv	1459	80

====================================================================================================

Data

train['Set'] = 'Train'
test['Set'] = 'Test'
Data = pd.concat([train,test])
Data = Data.reindex(sorted(Data.columns), axis=1)
del train, test

def Data_Plot(Inp, Title, W = None):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info = data_info.reset_index(drop = False).rename(columns = {'index':'Features'})
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type',
                 text = 'Percentage',
                 color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    if not W == None:
        fig.update_layout(width = W)
    fig.update_traces(texttemplate= 10*' ' + '%%{text}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    fig.update_layout(title={'text': '<b>' + Title + '<b>', 'x':0.5,
                                     'y':0.90, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()
    return data_info
    
def dtypes_group(Inp, Dict = False):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Features','Count'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Features'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
        Out.loc[Out.index == c, 'Count'] = len(Temp.loc[Temp['Data Type'] == c].index.tolist())
    Out.index.name = 'Data Type'
    Out = Out.reset_index(drop = False)
    Out['Data Type'] = Out['Data Type'].astype(str)
    
    if Dict:
        Out = dict(zip(Out['Data Type'], Out['Features']))
    return Out

data_info = Data_Plot(Data, Title = 'House Prices: Advanced Regression Techniques', W = None)
dType = dtypes_group(Data, Dict = True)

def List_Print(Text, List, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
            'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
            'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + '%s:' % Text + Style.RESET_ALL + ' %s' % ', '.join(List))
    
List_Print('Features with NaN values', data_info.loc[data_info['Number of NaN Values']>0, 'Features'].values)

Features with NaN values: Alley, BsmtCond, BsmtExposure, BsmtFinSF1, BsmtFinSF2, BsmtFinType1, BsmtFinType2, BsmtFullBath, BsmtHalfBath, BsmtQual, BsmtUnfSF, Electrical, Exterior1st, Exterior2nd, Fence, FireplaceQu, Functional, GarageArea, GarageCars, GarageCond, GarageFinish, GarageQual, GarageType, GarageYrBlt, KitchenQual, LotFrontage, MSZoning, MasVnrArea, MasVnrType, MiscFeature, PoolQC, SalePrice, SaleType, TotalBsmtSF, Utilities

Features

Data_Dict = {}

def Col_Details(Feat, Data = Data, Data_Dict = Data_Dict):
    return Data[Feat].replace(Data_Dict[Feat])

Alley: Type of alley access to property

Data['Alley'] = Data['Alley'].fillna('NA')
Data_Dict['Alley'] = {'Grvl':'Gravel', 'Pave':'Paved', 'NA': 'No Alley Access'}

Group = Col_Details('Alley', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	No Alley Access	Gravel	Paved
Count	2721	120	78

BldgType: Type of dwelling

Data_Dict['BldgType'] = {'1Fam':'Single-family Detached',
                         '2FmCon':'Two-family Conversion; originally built as one-family dwelling',
                         'Duplx':'Duplex','TwnhsE':'Townhouse End Unit','TwnhsI':'Townhouse Inside Unit'}

Group = Col_Details('BldgType', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Single-family Detached	Townhouse End Unit	Duplex	Twnhs	2fmCon
Count	2425	227	109	96	62

BsmtCond: Evaluates the general condition of the basement

Data['BsmtCond'] = Data['BsmtCond'].fillna('NA')
Data_Dict['BsmtCond'] = {'Ex':'Excellent','Gd':'Good','TA':'Typical - Slight Dampness Allowed',
                         'Fa':'Fair - Dampness or Some Cracking or Settling',
                         'Po':'Poor - Severe Cracking, Settling, or Wetness','NA':'No Basement'}
Group = Col_Details('BsmtCond', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Typical - Slight Dampness Allowed	2606
Good	122
Fair - Dampness or Some Cracking or Settling	104
No Basement	82
Poor - Severe Cracking, Settling, or Wetness	5

BsmtExposure: Refers to walkout or garden level walls

Data['BsmtExposure'] = Data['BsmtExposure'].fillna('NA')
Data_Dict['BsmtExposure'] = {'Gd':'Good Exposure',
                         'Av':'Average Exposure (split levels or foyers typically score average or above)',
                         'Mn':'Mimimum Exposure','No':'No Exposure','NA':'No Basement'}
Group = Col_Details('BsmtExposure', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
No Exposure	1904
Average Exposure (split levels or foyers typically score average or above)	418
Good Exposure	276
Mimimum Exposure	239
No Basement	82

BsmtFinSF1: Type 1 finished square feet

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['BsmtFinSF1'] = imp.fit_transform(Data['BsmtFinSF1'].values.reshape(-1, 1))
del imp

BsmtFinSF2: Type 2 finished square feet

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['BsmtFinSF2'] = imp.fit_transform(Data['BsmtFinSF2'].values.reshape(-1, 1))
del imp

BsmtFinType1: Rating of basement finished area

Data['BsmtFinType1'] = Data['BsmtFinType1'].fillna('NA')
Data_Dict['BsmtFinType1'] = {'GLQ':'Good Living Quarters','ALQ':'Average Living Quarters','BLQ':'Below Average Living Quarters',
                             'Rec':'Average Rec Room','LwQ':'Low Quality','Unf':'Unfinshed','NA':'No Basement'}
Group = Col_Details('BsmtFinType1', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Unfinshed	851
Good Living Quarters	849
Average Living Quarters	429
Average Rec Room	288
Below Average Living Quarters	269
Low Quality	154
No Basement	79

BsmtFinType2: Rating of basement finished area (if multiple types)

Data['BsmtFinType2'] = Data['BsmtFinType2'].fillna('NA')
Data_Dict['BsmtFinType2'] = {'GLQ':'Good Living Quarters','ALQ':'Average Living Quarters','BLQ':'Below Average Living Quarters',
                             'Rec':'Average Rec Room','LwQ':'Low Quality','Unf':'Unfinshed','NA':'No Basement'}
Group = Col_Details('BsmtFinType2', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Unfinshed	2493
Average Rec Room	105
Low Quality	87
No Basement	80
Below Average Living Quarters	68
Average Living Quarters	52
Good Living Quarters	34

BsmtFullBath: Basement full bathrooms

display(Data.loc[Data['BsmtFullBath'].isna() == True, Search_List('Bsmt', Data.columns)])
Data['BsmtFullBath'] = Data['BsmtFullBath'].fillna(0)

	BsmtCond	BsmtExposure	BsmtFinSF1	BsmtFinSF2	BsmtFinType1	BsmtFinType2	BsmtFullBath	BsmtHalfBath	BsmtQual	BsmtUnfSF	TotalBsmtSF
660	NA	NA	0.0	0.0	NA	NA	NaN	NaN	NaN	NaN	NaN
728	NA	NA	0.0	0.0	NA	NA	NaN	NaN	NaN	0.0	0.0

BsmtHalfBath: Basement half bathrooms

display(Data.loc[Data['BsmtHalfBath'].isna() == True, Search_List('Bsmt', Data.columns)])
Data['BsmtHalfBath'] = Data['BsmtHalfBath'].fillna(0)

	BsmtCond	BsmtExposure	BsmtFinSF1	BsmtFinSF2	BsmtFinType1	BsmtFinType2	BsmtFullBath	BsmtHalfBath	BsmtQual	BsmtUnfSF	TotalBsmtSF
660	NA	NA	0.0	0.0	NA	NA	0.0	NaN	NaN	NaN	NaN
728	NA	NA	0.0	0.0	NA	NA	0.0	NaN	NaN	0.0	0.0

BsmtQual: Evaluates the height of the basement

Data['BsmtQual'] = Data['BsmtQual'].fillna('NA')

Data_Dict['BsmtQual'] = {'Ex':'Excellent (100+ inches)','Gd':'Good (90-99 inches)','TA':'Typical (80-89 inches)',
                         'Fa':'Fair (70-79 inches)','Po':'Poor (<70 inches)','NA':'No Basement'}
Group = Col_Details('BsmtQual', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Typical (80-89 inches)	Good (90-99 inches)	Excellent (100+ inches)	Fair (70-79 inches)	No Basement
Count	1283	1209	258	88	81

BsmtUnfSF: Unfinished square feet of basement area

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['BsmtUnfSF'] = imp.fit_transform(Data['BsmtUnfSF'].values.reshape(-1, 1))
del imp

CentralAir: Central air conditioning

Data_Dict['CentralAir'] = {'N':'No','Y':'Yes'}
Group = Col_Details('CentralAir', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Yes	No
Count	2723	196

Condition1: Proximity to various conditions

Data_Dict['Condition1'] = {'Artery':'Adjacent to arterial street','Feedr':'Adjacent to feeder street','Norm':'Normal',
                           'RRNn':"Within 200' of North-South Railroad",'RRAn':'Adjacent to North-South Railroad',
                           'PosN':'Near positive off-site feature--park, greenbelt, etc.',
                           'PosA':'Adjacent to postive off-site feature','RRNe':"Within 200' of East-West Railroad",
                           'RRAe':'Adjacent to East-West Railroad'}
Group = Col_Details('Condition1', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Normal	2511
Adjacent to feeder street	164
Adjacent to arterial street	92
Adjacent to North-South Railroad	50
Near positive off-site feature--park, greenbelt, etc.	39
Adjacent to East-West Railroad	28
Adjacent to postive off-site feature	20
Within 200' of North-South Railroad	9
Within 200' of East-West Railroad	6

Condition2: Proximity to various conditions (if more than one is present)

Data_Dict['Condition2'] = {'Artery':'Adjacent to arterial street','Feedr':'Adjacent to feeder street','Norm':'Normal',
                           'RRNn':"Within 200' of North-South Railroad",'RRAn':'Adjacent to North-South Railroad',
                           'PosN':'Near positive off-site feature--park, greenbelt, etc.',
                           'PosA':'Adjacent to postive off-site feature','RRNe':"Within 200' of East-West Railroad",
                           'RRAe':'Adjacent to East-West Railroad'}
Group = Col_Details('Condition2', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Normal	2889
Adjacent to feeder street	13
Adjacent to arterial street	5
Adjacent to postive off-site feature	4
Near positive off-site feature--park, greenbelt, etc.	4
Within 200' of North-South Railroad	2
Adjacent to North-South Railroad	1
Adjacent to East-West Railroad	1

Electrical: Electrical system

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['Electrical'] = imp.fit_transform(Data['Electrical'].values.reshape(-1, 1))
del imp

Data_Dict['Electrical'] = {'SBrkr':'Standard Circuit Breakers & Romex',
                           'FuseA':'Fuse Box over 60 AMP and all Romex wiring (Average)',
                           'FuseF':'60 AMP Fuse Box and mostly Romex wiring (Fair)',
                           'FuseP':'60 AMP Fuse Box and mostly knob & tube wiring (poor)',
                           'Mix':'Mixed'}
Group = Col_Details('Electrical', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Standard Circuit Breakers & Romex	2672
Fuse Box over 60 AMP and all Romex wiring (Average)	188
60 AMP Fuse Box and mostly Romex wiring (Fair)	50
60 AMP Fuse Box and mostly knob & tube wiring (poor)	8
Mixed	1

ExterCond: Evaluates the present condition of the material on the exterior

Data_Dict['ExterCond'] = {'Ex':'Excellent','Gd':'Good','TA':'Average/Typical','Fa':'Fair','Po':'Poor'}
Group = Col_Details('ExterCond', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Average/Typical	Good	Fair	Excellent	Poor
Count	2538	299	67	12	3

ExterQual: Evaluates the quality of the material on the exterior

Data_Dict['ExterQual'] = {'Ex':'Excellent','Gd':'Good','TA':'Average/Typical','Fa':'Fair','Po':'Poor'}
Group = Col_Details('ExterQual', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Average/Typical	Good	Excellent	Fair
Count	1798	979	107	35

Exterior1st: Exterior covering on house

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['Exterior1st'] = imp.fit_transform(Data['Exterior1st'].values.reshape(-1, 1))
del imp
Data_Dict['Exterior1st'] = {'AsbShng':'Asbestos Shingles','AsphShn':'Asphalt Shingles','BrkComm':'Brick Common',
                            'BrkFace':'Brick Face','CBlock':'Cinder Block','CemntBd':'Cement Board','HdBoard':'Hard Board',
                            'ImStucc':'Imitation Stucco','MetalSd':'Metal Siding','Other':'Other','Plywood':'Plywood',
                            'PreCast':'PreCast','Stone':'Stone','Stucco':'Stucco','VinylSd':'Vinyl Siding',
                            'Wd Sdng':'Wood Siding','WdShing':'Wood Shingles'}
Group = Col_Details('Exterior1st', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Vinyl Siding	1026
Metal Siding	450
Hard Board	442
Wood Siding	411
Plywood	221
Cement Board	126
Brick Face	87
Wood Shingles	56
Asbestos Shingles	44
Stucco	43
Brick Common	6
Asphalt Shingles	2
Stone	2
Cinder Block	2
Imitation Stucco	1

Exterior2nd: Exterior covering on house (if more than one material)

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['Exterior2nd'] = imp.fit_transform(Data['Exterior2nd'].values.reshape(-1, 1))
del imp
Data_Dict['Exterior2nd'] = {'AsbShng':'Asbestos Shingles','AsphShn':'Asphalt Shingles','BrkComm':'Brick Common',
                            'BrkFace':'Brick Face','CBlock':'Cinder Block','CemntBd':'Cement Board','HdBoard':'Hard Board',
                            'ImStucc':'Imitation Stucco','MetalSd':'Metal Siding','Other':'Other','Plywood':'Plywood',
                            'PreCast':'PreCast','Stone':'Stone','Stucco':'Stucco','VinylSd':'Vinyl Siding',
                            'Wd Sdng':'Wood Siding','WdShing':'Wood Shingles'}
Group = Col_Details('Exterior2nd', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Vinyl Siding	1015
Metal Siding	447
Hard Board	406
Wood Siding	391
Plywood	270
CmentBd	126
Wd Shng	81
Stucco	47
Brick Face	47
Asbestos Shingles	38
Brk Cmn	22
Imitation Stucco	15
Stone	6
Asphalt Shingles	4
Cinder Block	3
Other	1

Fence: Fence quality

Data['Fence'] = Data['Fence'].fillna('NA')

Data_Dict['Fence'] = {'GdPrv':'Good Privacy','MnPrv':'Minimum Privacy','GdWo':'Good Wood','MnWw':'Minimum Wood/Wire',
                      'NA':'No Fence'}
Group = Col_Details('Fence', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	No Fence	Minimum Privacy	Good Privacy	Good Wood	Minimum Wood/Wire
Count	2348	329	118	112	12

FireplaceQu: Fireplace quality

Data['FireplaceQu'] = Data['FireplaceQu'].fillna('NA')
Data_Dict['FireplaceQu'] = {'Ex':'Excellent - Exceptional Masonry Fireplace',
                           'Gd':'Good - Masonry Fireplace in main level',
                           'TA':'Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement',
                           'Fa':'Fair - Prefabricated Fireplace in basement',
                           'Po':'Poor - Ben Franklin Stove',
                           'NA':'No Fireplace'}
Group = Col_Details('FireplaceQu', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
No Fireplace	1420
Good - Masonry Fireplace in main level	744
Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement	592
Fair - Prefabricated Fireplace in basement	74
Poor - Ben Franklin Stove	46
Excellent - Exceptional Masonry Fireplace	43

Foundation: Type of foundation

Data_Dict['Foundation'] = {'BrkTil':'Brick & Tile','CBlock':'Cinder Block','PConc':'Poured Contrete','Slab':'Slab',
                           'Stone':'Stone','Wood':'Wood'}
Group = Col_Details('Foundation', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Poured Contrete	Cinder Block	Brick & Tile	Slab	Stone	Wood
Count	1308	1235	311	49	11	5

Functional: Home functionality (Assume typical unless deductions are warranted)

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['Functional'] = imp.fit_transform(Data['Functional'].values.reshape(-1, 1))
del imp
Data_Dict['Functional'] = {'Typ':'Typical Functionality','Min1':'Minor Deductions 1','Min2':'Minor Deductions 2',
                           'Mod':'Moderate Deductions','Maj1':'Major Deductions 1','Maj2':'Major Deductions 2',
                           'Sev':'Severely Damaged','Sal':'Salvage only'}
Group = Col_Details('Functional', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Typical Functionality	Minor Deductions 2	Minor Deductions 1	Moderate Deductions	Major Deductions 1	Major Deductions 2	Severely Damaged
Count	2719	70	65	35	19	9	2

GarageArea: Size of garage in square feet

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['GarageArea'] = imp.fit_transform(Data['GarageArea'].values.reshape(-1, 1))
del imp

GarageCars: Size of garage in car capacity

Data['GarageCars'] = Data['GarageCars'].fillna(0).astype(int)

GarageCond: Garage condition

Data['GarageCond'] = Data['GarageCond'].fillna('NA')
Data_Dict['GarageCond'] = {'Ex':'Excellent','Gd':'Good','TA':'Typical/Average','Fa':'Fair','Po':'Poor','NA':'No Garage'}
Group = Col_Details('GarageCond', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Typical/Average	No Garage	Fair	Good	Poor	Excellent
Count	2654	159	74	15	14	3

GarageFinish: Interior finish of the garage

Data['GarageFinish'] = Data['GarageFinish'].fillna('NA')
Data_Dict['GarageFinish'] = {'Fin':'Finished','RFn':'Rough Finished','Unf':'Unfinished','NA':'No Garage'}
Group = Col_Details('GarageFinish', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Unfinished	Rough Finished	Finished	No Garage
Count	1230	811	719	159

GarageQual: Garage quality

Data['GarageQual'] = Data['GarageQual'].fillna('NA')
Data_Dict['GarageQual'] = {'Ex':'Excellent','Gd':'Good','TA':'Typical/Average','Fa':'Fair','Po':'Poor','NA':'No Garage'}
Group = Col_Details('GarageQual', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Typical/Average	No Garage	Fair	Good	Poor	Excellent
Count	2604	159	124	24	5	3

GarageType: Garage location

Data['GarageType'] = Data['GarageType'].fillna('NA')
Data_Dict['GarageType'] = {'2Types':'More than one type of garage','Attchd':'Attached to home','Basment':'Basement Garage',
                           'BuiltIn':'Built-In (Garage part of house - typically has room above garage)',
                           'CarPort':'Car Port','Detchd':'Detached from home','NA':'No Garage'}
Group = Col_Details('GarageType', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Attached to home	Detached from home	Built-In (Garage part of house - typically has room above garage)	No Garage	Basement Garage	More than one type of garage	Car Port
Count	1723	779	186	157	36	23	15

GarageYrBlt: Year garage was built

Data['GarageYrBlt'] = Data['GarageYrBlt'].fillna(-1).astype(int)

Heating: Type of heating

Data_Dict['Heating'] = {'Floor':'Floor Furnace','GasA':'Gas forced warm air furnace','GasW':'Gas hot water or steam heat',
                        'Grav':'Gravity furnace','OthW':'Hot water or steam heat other than gas','Wall':'Wall furnace'}
Group = Col_Details('Heating', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Gas forced warm air furnace	Gas hot water or steam heat	Gravity furnace	Wall furnace	Hot water or steam heat other than gas	Floor Furnace
Count	2874	27	9	6	2	1

HeatingQC: Heating quality and condition

Data_Dict['HeatingQC'] = {'Ex':'Excellent','Gd':'Good','TA':'Average/Typical','Fa':'Fair','Po':'Poor'}
Group = Col_Details('HeatingQC', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Excellent	Average/Typical	Good	Fair	Poor
Count	1493	857	474	92	3

HouseStyle: Style of dwelling

Data_Dict['HouseStyle'] = {'1Story':'One story','1.5Fin':'One and one-half story: 2nd level finished',
                           '1.5Unf':'One and one-half story: 2nd level unfinished',
                           '2Story':'Two story','2.5Fin':'Two and one-half story: 2nd level finished',
                           '2.5Unf':'Two and one-half story: 2nd level unfinished','SFoyer':'Split Foyer','SLvl':'Split Level'}

Group = Col_Details('HouseStyle', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
One story	1471
Two story	872
One and one-half story: 2nd level finished	314
Split Level	128
Split Foyer	83
Two and one-half story: 2nd level unfinished	24
One and one-half story: 2nd level unfinished	19
Two and one-half story: 2nd level finished	8

KitchenQual: Kitchen quality

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['KitchenQual'] = imp.fit_transform(Data['KitchenQual'].values.reshape(-1, 1))
del imp
Data_Dict['KitchenQual'] = {'Ex':'Excellent','Gd':'Good','TA':'Typical/Average','Fa':'Fair','Po':'Poor'}
Group = Col_Details('KitchenQual', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Typical/Average	Good	Excellent	Fair
Count	1493	1151	205	70

LandContour: Flatness of the property

Data_Dict['LandContour'] = {'Lvl':'Near Flat/Level','Bnk':'Banked - Quick and significant rise from street grade to building',
                            'HLS':'Hillside - Significant slope from side to side','Low':'Depression'}
Group = Col_Details('LandContour', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Near Flat/Level	2622
Hillside - Significant slope from side to side	120
Banked - Quick and significant rise from street grade to building	117
Depression	60

LandSlope: Slope of property

Data_Dict['LandSlope'] = {'Gtl':'Gentle Slope','Mod':'Moderate Slope','Sev':'Severe Slope'}
Group = Col_Details('LandSlope', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Gentle Slope	Moderate Slope	Severe Slope
Count	2778	125	16

LotConfig: Lot configuration

Data_Dict['LotConfig'] = {'Inside':'Inside lot','Corner':'Corner lot','CulDSac':'Cul-de-sac',
                          'FR2':'Frontage on 2 sides of property','FR3':'Frontage on 3 sides of property'}
Group = Col_Details('LotConfig', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
Inside lot	2133
Corner lot	511
Cul-de-sac	176
Frontage on 2 sides of property	85
Frontage on 3 sides of property	14

MSZoning: Identifies the general zoning classification of the sale

Data['MSZoning'] = Data['MSZoning'].fillna(Data.loc[(Data['LotFrontage'] >=109) &
                                                    (Data['LotFrontage'] <=125), 'MSZoning'].mode()[0])
Data_Dict['MSZoning'] = {'A':'Agriculture','C':'Commercial','FV':'Floating Village Residential','I':'Industrial',
                         'RH':'Residential High Density','RL':'Residential Low Density','RP':'Residential Low Density Park',
                         'RM':'Residential Medium Density'}
Data['MSZoning'] = Data['MSZoning'].replace({'C (all)':'C'})

Group = Col_Details('MSZoning')
display(Group.value_counts().to_frame('Count'))

	Count
Residential Low Density	2269
Residential Medium Density	460
Floating Village Residential	139
Residential High Density	26
Commercial	25

LotFrontage: Linear feet of street connected to property

mylist = Data['MSZoning'].unique()
for x in mylist:
    imp = SimpleImputer(missing_values=np.nan, strategy='mean')
    Data.loc[Data['MSZoning'] == x, 'LotFrontage'] = \
    imp.fit_transform(Data.loc[Data['MSZoning'] == x, 'LotFrontage'].values.reshape(-1, 1))
    del imp

LotShape: General shape of property

Data_Dict['LotShape'] = {'Reg':'Regular', 'IR1': 'Slightly irregular', 'IR2': 'Moderately Irregular', 'IR3': 'Irregular'}
Group = Col_Details('LotShape', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Regular	Slightly irregular	Moderately Irregular	Irregular
Count	1859	968	76	16

MSSubClass: Identifies the type of dwelling involved in the sale

Data_Dict['MSSubClass'] = {20:'1-Story 1946 & Newer All Styles', 30:'1-Story 1945 & Older',
                           40:'1-Story W/Finished Attic All Ages',45:'1-1/2 Story - Unfinished All Ages',
                           50:'1-1/2 Story Finished All Ages',60:'2-Story 1946 & Newer',70:'2-Story 1945 & Older',
                           75:'2-1/2 Story All Ages',80:'Split Or Multi-Level',85:'Split Foyer',
                           90:'Duplex - All Styles And Ages',120:'1-Story Pud (Planned Unit Development) - 1946 & Newer',
                           150:'1-1/2 Story Pud - All Ages',160:'2-Story Pud - 1946 & Newer',
                           180:'Pud - Multilevel - Incl Split Lev/Foyer',190:'2 Family Conversion - All Styles And Ages'}

def Col_Details(Feat, Data = Data, Data_Dict = Data_Dict):
    return Data[Feat].replace(Data_Dict[Feat])

Group = Col_Details('MSSubClass')
display(Group.value_counts().to_frame('Count'))

	Count
1-Story 1946 & Newer All Styles	1079
2-Story 1946 & Newer	575
1-1/2 Story Finished All Ages	287
1-Story Pud (Planned Unit Development) - 1946 & Newer	182
1-Story 1945 & Older	139
2-Story Pud - 1946 & Newer	128
2-Story 1945 & Older	128
Split Or Multi-Level	118
Duplex - All Styles And Ages	109
2 Family Conversion - All Styles And Ages	61
Split Foyer	48
2-1/2 Story All Ages	23
1-1/2 Story - Unfinished All Ages	18
Pud - Multilevel - Incl Split Lev/Foyer	17
1-Story W/Finished Attic All Ages	6
1-1/2 Story Pud - All Ages	1

MasVnrArea: Masonry veneer area in square feet

Data['MasVnrArea'] = Data['MasVnrArea'].fillna(0)

MasVnrType: Masonry veneer type

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['MasVnrType'] = imp.fit_transform(Data['MasVnrType'].values.reshape(-1, 1))
del imp
# Data['MasVnrType'] = Data['MasVnrType'].fillna(0)
Data_Dict['MasVnrType'] = {'BrkCmn':'Brick Common','BrkFace':'Brick Face','CBlock':'Cinder Block','None':'None',
                            'Stone':'Stone'}
Group = Col_Details('MasVnrType', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	None	Brick Face	Stone	Brick Common
Count	1766	879	249	25

MiscFeature: Miscellaneous feature not covered in other categories

Data['MiscFeature'] = Data['MiscFeature'].fillna('NA')
Data_Dict['MiscFeature'] = {'Elev':'Elevator','Gar2':'2nd Garage (if not described in garage section)','Othr':'Other',
                           'Shed':'Shed (over 100 SF)','TenC':'Tennis Court','NA':'None'}
Group = Col_Details('MiscFeature', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	None	Shed (over 100 SF)	2nd Garage (if not described in garage section)	Other	Tennis Court
Count	2814	95	5	4	1

Neighborhood: Physical locations within Ames city limits

Data['Neighborhood'] = Data['Neighborhood'].replace({'NAmes':'Names'})
Data_Dict['Neighborhood'] = {'Blmngtn':'Bloomington Heights','Blueste':'Bluestem','BrDale':'Briardale','BrkSide':'Brookside',
                             'ClearCr':'Clear Creek','CollgCr':'College Creek','Crawfor':'Crawford','Edwards':'Edwards',
                             'Gilbert':'Gilbert','IDOTRR':'Iowa DOT and Rail Road','MeadowV':'Meadow Village',
                             'Mitchel':'Mitchell','Names':'North Ames','NoRidge':'Northridge','NPkVill':'Northpark Villa',
                             'NridgHt':'Northridge Heights','NWAmes':'Northwest Ames','OldTown':'Old Town',
                             'SWISU':'South & West of Iowa State University','Sawyer':'Sawyer','SawyerW':'Sawyer West',
                             'Somerst':'Somerset','StoneBr':'Stone Brook','Timber':'Timberland','Veenker':'Veenker'}
Group = Col_Details('Neighborhood', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
North Ames	443
College Creek	267
Old Town	239
Edwards	194
Somerset	182
Northridge Heights	166
Gilbert	165
Sawyer	151
Northwest Ames	131
Sawyer West	125
Mitchell	114
Brookside	108
Crawford	103
Iowa DOT and Rail Road	93
Timberland	72
Northridge	71
Stone Brook	51
South & West of Iowa State University	48
Clear Creek	44
Meadow Village	37
Briardale	30
Bloomington Heights	28
Veenker	24
Northpark Villa	23
Bluestem	10

OverallCond: Rates the overall condition of the house

Data_Dict['OverallQual'] = {10:'Very Excellent',9:'Excellent',8:'Very Good',7:'Good',6:'Above Average',5:'Average',
                            4:'Below Average',3:'Fair',2:'Poor',1:'Very Poor'}
Group = Col_Details('OverallQual', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Average	Above Average	Good	Very Good	Below Average	Excellent	Fair	Very Excellent	Poor	Very Poor
Count	825	731	600	342	226	107	40	31	13	4

PavedDrive: Paved driveway

Data_Dict['PavedDrive'] = {'Y':'Paved','P':'Partial Pavement','N':'Dirt/Gravel'}
Group = Col_Details('PavedDrive', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Paved	Dirt/Gravel	Partial Pavement
Count	2641	216	62

PoolQC: Pool quality

Data['PoolQC'] = Data['PoolQC'].fillna('NA')
Data_Dict['PoolQC'] = {'Ex':'Excellent','Gd':'Good','TA':'Average/Typical','Fa':'Fair','NA':'No Pool'}
Group = Col_Details('PoolQC', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	No Pool	Excellent	Good	Fair
Count	2909	4	4	2

RoofMatl: Roof material

Data_Dict['RoofMatl'] = {'ClyTile':'Clay or Tile','CompShg':'Standard (Composite) Shingle','Membran':'Membrane','Metal':'Metal',
                         'Roll':'Roll','Tar&Grv':'Gravel & Tar','WdShake':'Wood Shakes','WdShngl':'Wood Shingles'}
Group = Col_Details('RoofMatl', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Standard (Composite) Shingle	Gravel & Tar	Wood Shakes	Wood Shingles	Membrane	Roll	Clay or Tile	Metal
Count	2876	23	9	7	1	1	1	1

RoofStyle: Type of roof

Data_Dict['RoofStyle'] = {'Flat':'Flat','Gable':'Gable','Gambrel':'Gabrel (Barn)','Hip':'Hip','Mansard':'Mansard','Shed':'Shed'}
Group = Col_Details('RoofStyle', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Gable	Hip	Gabrel (Barn)	Flat	Mansard	Shed
Count	2310	551	22	20	11	5

SaleCondition: Condition of sale

Data_Dict['SaleCondition'] = {'Normal':'Normal Sale','Abnorml':'Abnormal Sale -  trade, foreclosure, short sale',
                          'AdjLand':'Adjoining Land Purchase',
                          'Alloca':'Allocation - two linked properties with separate deeds, typically condo with a garage unit',
                          'Family':'Sale between family members',
                          'Partial':'Home was not completed when last assessed (associated with New Homes)'}
Group = Col_Details('SaleCondition', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Normal Sale	Home was not completed when last assessed (associated with New Homes)	Abnormal Sale - trade, foreclosure, short sale	Sale between family members	Allocation - two linked properties with separate deeds, typically condo with a garage unit	Adjoining Land Purchase
Count	2402	245	190	46	24	12

SaleType: Type of sale

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['SaleType'] = imp.fit_transform(Data['SaleType'].values.reshape(-1, 1))
del imp
Data_Dict['SaleType'] = {'WD':'Warranty Deed - Conventional','CWD':'Warranty Deed - Cash','VWD':'Warranty Deed - VA Loan',
                         'New':'Home just constructed and sold','COD':'Court Officer Deed/Estate',
                         'Con':'Contract 15% Down payment regular terms','ConLw':'Contract Low Down payment and low interest',
                         'ConLI':'Contract Low Interest','ConLD':'Contract Low Down','Oth':'Other'}
Group = Col_Details('SaleType', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Warranty Deed - Conventional	Home just constructed and sold	Court Officer Deed/Estate	Contract Low Down	Warranty Deed - Cash	Contract Low Interest	Contract Low Down payment and low interest	Other	Contract 15% Down payment regular terms
Count	2526	239	87	26	12	9	8	7	5

Street: Type of road access to property

Data_Dict['Street'] = {'Grvl':'Gravel', 'Pave':'Paved'}
Group = Col_Details('Street', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count').T)

	Paved	Gravel
Count	2907	12

TotalBsmtSF: Total square feet of basement area

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['TotalBsmtSF'] = imp.fit_transform(Data['TotalBsmtSF'].values.reshape(-1, 1))
del imp

Utilities: Type of utilities available

imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
Data['Utilities'] = imp.fit_transform(Data['Utilities'].values.reshape(-1, 1))
del imp
Data_Dict['Utilities'] = {'AllPub':'All public Utilities (E,G,W,& S)','NoSewr':'Electricity, Gas, and Water (Septic Tank)',
                          'NoSeWa':'Electricity and Gas Only', 'ELO':'Electricity only'}
Group = Col_Details('Utilities', Data = Data, Data_Dict = Data_Dict)
display(Group.value_counts().to_frame('Count'))

	Count
All public Utilities (E,G,W,& S)	2918
Electricity and Gas Only	1

Final Output

_ = Data_Plot(Data, Title = 'House Prices: Advanced Regression Techniques', W = None)
Data = Data.rename(columns = {'Id':'ID'})
Data.to_csv(PATH + '/House_Prices.csv' , index = None, header=True)
with open(PATH + '/House_Prices_Data_Dict.pkl', 'wb') as fp:
    pickle.dump(Data_Dict, fp, protocol=pickle.HIGHEST_PROTOCOL)

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References

1. Kaggle Dataset: House Prices - Advanced Regression Techniques

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